確率的コンセンサスに基づくマルチロボット目標探索：離散マルコフ連鎖アプローチ
Multi-Robot Target Search using Probabilistic Consensus on Discrete Markov Chains
通信障害に強いマルチロボット探索手法を提案し、局所通信とランダムウォークによる情報交換で静的目標の存在に関する合意形成を理論・シミュレーションで検証した論文。
著者: Aniket Shirsat, Karthik Elamvazhuthi, Spring Berman
分類: cs.RO, cs.MA
原文アブストラクト
In this paper, we propose a probabilistic consensus-based multi-robot search strategy that is robust to communication link failures, and thus is suitable for disaster affected areas. The robots, capable of only local communication, explore a bounded environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and exchange information with neighboring robots, resulting in a time-varying communication network topology. The proposed strategy is proved to achieve consensus, here defined as agreement on the presence of a static target, with no assumptions on the connectivity of the communication network. Using numerical simulations, we investigate the effect of the robot population size, domain size, and information uncertainty on the consensus time statistics under this scheme. We also validate our theoretical results with 3D physics-based simulations in Gazebo. The simulations demonstrate that all robots achieve consensus in finite time with the proposed search strategy over a range of robot densities in the environment.
